How to create an AI Application

  • Improve consumer analytics and customer behavior’s prediction, reduce the research cost and make it deep and comprehensive;
  • Reach the higher security and data loss prevention;
  • Improve overall performance and analyze vast amounts faster and with lower costs of computing resources;
  • Get a better user experience, optimize customer searches, and increase customer loyalty;
  • Integrate your app with IoT to expand the product scope, and therefore its customer base; and
  • Reduce personnel and training costs and minimize human factor risks.
  • Microsoft Cognitive Toolkit (CNTK);
  • Amazon Machine Learning (AML);
  • Apple’s Core ML/Create ML;
  • PyTorch, Caffe2, Keras, Scikit-learn, etc.
  • Azure Topic Detection API excellently recognizes unstructured text using NLP and gives a deeper understanding of customer opinions through sentiment analysis;
  • Microsoft Face API and Google Vision API help you easily integrate facial recognition for a convenient and reliable user experience; and
  • Apple’s SiriKit that processes voice requests into app actions and displays branding and custom content in Siri or Maps.
  • Discovery, requirements’ definition, and planning;
  • Data mining and modeling;
  • Minimum viable product (MVP) and final app’s development; and
  • Testing and delivery.
  • Collect the data and prepare it to receive an accurate model,
  • Create and train ML model;
  • Test, evaluate, deploy the model and load it into the app;
  • Build an MVP and full-fledged app: think over the architecture, create the user interface, frontend and backend;
  • Test the finished app with QA engineering tools.



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